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Integration of the African Stock Markets to the Global Markets:
Case Study of South Africa1
Forget Mingiri Kapingura
University of Fort Hare, Department of Economics
East London campus, East London, South Africa
fkapingura@ufh.ac.za
Syden Mishi
University of Fort Hare, Department of Economics
East London campus, East London, South Africa
smishi@ufh.ac.za
Sibanisezwe Khumalo
University of Fort Hare, Department of Economics
East London campus, East London, South Africa
skhumalo@ufh.ac.za
Doi:10.5901/mjss.2014.v5n2p619
Abstract
The paper empirically investigates the extent to which the South African stock market is integrated to other African stock
market as well as the developed markets as represented by the US, Japan and German. The study employs the
Autoregressive Distributed Lag (ARDL) approach to cointegration because it is a more reliable method than other conventional
cointegration approaches and is also applicable even if the series are integrated of different orders, as long as none is of order
2 or more. It is also more robust and performs well for small sample sizes unlike the conventional approaches which are valid
for large sample sizes. The results of empirical tests suggest that the South African stock market is fully integrated to the
developed markets. However, the South African stock market is not fully integrated to other African stock markets. This
suggests that investors can diversify their portfolios by investing in other African stock markets.
Keywords: Stock market, Integration, Autoregressive Distributed Lag, Cointegration
1. Introduction
Integration among stock markets has received considerable attention by policy makers as well as finance specialists.
There are a number of reasons to support this move. Firstly, it can be argued that it provides opportunities in risk sharing
among integrated markets (Marashdeh & Shrestha, 2010). In addition, it contributes to financial stability by enhancing
competition and efficiency in allocation of resources, as well as reducing the cost of capital and price volatility among
integrated markets (Tai, 2007). Integration of stock markets also plays an important role in promoting domestic savings,
investment and could positively affect total factor productivity and economic growth (Levine, 2001). However, there are a
number of studies which does not support stock market integration. Most notably those that argue that stock market
integration could pose a major risk of contagion as was evidenced during the Asian crisis of 1997 and the global financial
crisis.
It is important to note that stock markets in Africa have received little attention due to under development and
illiquidity of the markets. However, there has been considerable improvement, rapid growth and liberalisation in a number
of African countries stock markets (Allen et al, 2010; Allen, Otchere and Senbet, 2010).
It is behind this background that this study aims at identifying the long-run relationship and linkages of the South
First draft prepared and Presented at the Fourth International Conference on Accounting and Finance (ICAF4), Windhoek, Namibia
(09 – 10 October 2012)
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African stock market proxied by key indices to the developed stock markets; and selected African countries. The choice
of South Africa lies in that the market is well developed and liquid relative to other markets in Africa, and an analysis of its
integration with different market sizes and from different regions enhances our conclusions adding value to the available
literature.
The paper is organised as follows. Section II presents an overview of the performance of African stock markets.
Section III reviews the theoretical and empirical literature. Section IV describes the data employed in the study, the
methodology for the empirical analysis and the empirical results. Finally, section V presents the Summary and concluding
remarks of the study.
2. Overview of the African Stock Markets
Allen, Otchere and Senbet (2010) carried out a comprehensive study on Africa’s financial systems. The report indicates
that there has been a surge in the establishment of stock exchanges, particularly in Sub-Saharan Africa (SSA). Also, the
report indicates that there were 5 stock exchanges in SSA about two decades ago, but now there are about 20 in
operation. SSA also witnessed an establishment of a regional stock exchange domiciled in Abidjan, serving mostly the
Francophone countries of West Africa. Similar initiatives are underway in Southern and Eastern Africa with the aim to
consolidate the thinly capitalized markets into regional markets. This section will provide an analysis of the development
of African stock markets.
2.1 Depth of African Stock Markets
African stock markets face serious challenges in terms of depth as measured by market capitalization and listing (Allen et
al, 2010). Allen et at (2010) further indicates that with the exception of South Africa and Egypt, the stock markets in Africa
are the smallest of any region in terms of both listed companies and market capitalization. This is illustrated in table 2.1
and 2.2.
Table 2.1 indicates that the mean capitalization for each period has been on the increase for all regions. Because
of South Africa and Egypt, the Southern Africa and Northern Africa markets have relatively higher market capitalization.
However, the 2008 financial crisis led to a drop in market capitalization. For South Africa and Egypt, which are the largest
markets in Africa, they dropped by about 40 and 50% respectively.
With regards to listing on the African stock markets, table 2.2 indicates that the number of firms on the African
stock markets is few. Senbet and Otchere (2009) show that in 2007 the mean number of firms listed on the African stock
markets was 129 far less as compared with individual countries such as Malaysia with 911 first listed and 158 companies
in Mexico.
2.2 Liquidity of African Stock Markets
Market liquidity is defined as the ability to execute transactions of a representative size cheaply and rapidly without
having too much of an effect on price. This element is of great importance to the stock market as it determines the
number of participants who will be interested in investing on the market. There are a number of proxy measures of
liquidity which include the bid-ask spread, turnover per year and the turnover as a ratio of market capitalisation. As
measured by the total value of shares traded scaled by GDP, table 2.3 illustrates that African stock markets are highly
illiquid and thin despite the rapid growth of the stock exchanges in Africa over the past 20 years. As indicated in table 2.3,
the value of stock traded as a percentage of GDP is small even though it has been increasing. East African markets are
the most illiquid as the value of stock traded is less than 1% of GDP (Allen et al, 2010).
2.3 Performance of African Stock markets
African stock markets have performed remarkably well in terms of absolute returns (Allen et al, 2010). This is illustrated
in table 2.4.
Table 2.4 illustrates the performance of the African stock markets. The table indicates that the average annual
returns for these markets over a period of ten years was 25% with the returns in some other countries such as Egypt and
Malawi at times exceeding 100.
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3. Theoretical Background and Literature Review
The law of one price (LOOP), pioneered by Augustin Cournot (1927) and Alfred Marshall (1930), constitutes the
fundamental principle underlying financial market integration. While the LOOP provides a generalised framework for
financial market integration, finance literature provides alternative principles, which establish operational linkages among
different financial market segments.
In general, financial/ stock market integration occurs in three dimensions, nationally, regionally and globally (BIS,
2006). From an alternative perspective, stock market integration could take place horizontally and vertically. In the
horizontal integration, inter-linkages occur among domestic stock market segments, while vertical integration occurs
between domestic markets and international/financial markets (USAID, 1998). This study focuses on vertical integration
of African stock markets and the developed economies counterparts, that is, a global dimension.
Stock markets provide an important opportunity for integrating Africa into the global financial market place and
attracting global capital, (Senbet and Otchere, 2008).
According to Altin and Sahin (2010), the question of stock market integration had started to rise in the 1970’s with
the waning of capital market restrictions between developed economies. This dates back to Ripley (1973) study on
interdependencies between 19 open markets. Stock market integration and interdependencies have implications for
international investors and fund managers because the degree of market integration affects the benefits of international
diversification. In essence, better stock market integration is also of interest to policy makers in the sense that events in
one market can have significant effects in other markets, as each stock market becomes an integral part of a single
global market. The popularity of this area of study is increasing given the economic challenges developed economies can
be facing; then how can these be transmitted to African markets.
Informatively, Pretorius (2002) divides the stock market integration literature surveying into three categories: How
they are interdependent, possible changes and why they are. This study aim to provide and answer to how African and
global markets are interdependent and why is that so, that is answering Pretorius first and third question as in Ripley
(1973).
It has been noted in literature that information variables are key in predicting developing market returns than with
developed markets. Harvey (1995) founded correlation between developed markets and US, on the other hand
concluding that there is no significant correlation between US and emerging markets. The main reason being that local
information variables play an important role in predicting emerging market returns.
According to Lovegrove (2007), financial intermediaries and financial systems in Africa suffer from diseconomies
caused by small scale. The study further argued that such diseconomies can be overcome by opening financial systems
through regional financial integration, for example, to increase scale. The rationale behind the argument is that financial
services in small systems tend to be more limited in scope, more expensive, and of poorer quality than services in larger
systems. In such instances the case for integration of stock markets flows naturally.
Agyei-Ampomah (2008) considers the nature and extent of linkages between African stock markets and the
relationships between these markets and that of regional and global indices between 1998-2007. Monthly returns of
S&P/IFC return indices for 10 African countries, establishing that that African stock markets are still segmented from
global markets in spite of some structural adjustments and that the local index volatility is largely country-specific which
can be diversified away by cross-country diversification. The authors further argue that the structural adjustment of that
time and liberalisation policies have not reduced stock market segmentation in Africa. It is imperative to investigate the
same problem, with extend time frame and major events having taken place (for example global financial crisis) and
advanced estimation techniques. In a time of increasing globalization, the transmission of movements in international
financial markets is a central issue for economic policy, especially in episodes where markets are highly volatile.
On the other hand, Alagidebe (2008) investigated the integration of African stock markets into the global financial
system and the implications for investment analysis and risk sharing. Using co-integration techniques, the study firstly
showed that African stock markets are not well integrated with each other. Furthermore, the study revealed that there are
weak stochastic trends between African markets and the rest of the world, indicating that Africa’s markets tend to
respond to local rather than global information. Although the weak trends uncovered present an opportunity to diversify
portfolio into African markets, the argument was that risk perception and institutional underdevelopment remain obstacles
to the development of Africa’s emerging equity markets.
Aponsah (2009) presented a critical review of the foundational policies that should be in place to enable the full
and deep regional integration of African countries. While, Jeferis and Smith (2005) note that, with the exception of the
Johannesburg Stock Exchange, most of the SSA markets are relatively small in terms of capitalization and turnover and
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number of stocks. Most, including Johannesburg, are also illiquid by global standards; this has implications for market
efficiency because liquidity has been found to be one of the most important links between stock markets and economic
growth. The study finds that there is evidence of linkage between the BSE and the JSE, but little evidence of linkage
between the ZSE and the other two. This empirical evidence supports the historical record of relationships among the
countries. Botswana and South Africa have a highly open economic relationship; they are partners in the Southern Africa
Customs Union (SACU) and are important trading partners (75% of Botswana's imports come from South Africa). A
second conclusion is that the southern African markets, both individually and as a group, are not closely linked either to
the two major developed country stock markets (the USA and the UK) or to other emerging stock markets. In terms of
short-term relationships, the JSE appears to be more closely linked that the BSE or ZSE to the emerging and developed
markets, likely because of the larger size and more efficient operation of the JSE. The lack of linkages between southern
African and other international markets suggests that the region will continue to experience capital inflows, as fund
managers seek the international diversification of risk that these markets appear to offer. However, the relationship
between the JSE and the BSE suggests that there may be little diversification benefit between the two markets, which
will tend to work against Botswana.
Altin and Sahin (2010) argue that globalization is the main topic of social sciences and stock market linkage has
become one of the most important issue of financial and economic studies. The study focused on testifying the stock
market linkage in regional context, considering 31 behaviours of stock markets. Employing con integration and granger
causality tests, among others, the study utilised 2005 – 2010 daily data of all the stock markets and examines the short
and long – run relationships between intra – regional movements. Those regions are Europe, Asia/Pacific and America.
4. Data, Descriptive Statistics and Methodology for Empirical Analysis
The study employed monthly stock price indices for a period ranging from January 2000 to December 2010. These
indices are for South Africa, Nigeria, Botswana, Egypt and Mauritius. The five countries were chosen on the basis of data
availability. Monthly stock price indices for the US, Germany and Japan represented the developed markets. The stock
price data for the African stock markets and the US, German and Japan was collected from Bloomberg. The rationale for
using monthly data lies in that it avoids distortions which are common in weekly and daily data which arise because of
non-trading and non-synchronous trading (Marashdeh and Shrestha, 2010).
We estimated a model in which the Johannesburg Stock market (JSE) is the dependent variable while the,
Nigerian stock market, Botswana stock market, Egypt Stock market and Mauritius stock market, USA stock market,
Germany and Japan Stock markets.
To analyse the relationship between South Africa’s stock market and the selected African stock markets as well as
the developed markets the study relied on a model developed by Marashdeh and Shrestha (2010). The model employed
is based on the following general model:
y = α + βi X i + u
4.1
where,
y-Stock market as dependent variable
Xi – Stock markets as independent variables
The Error Correction representation of the model can be represented as follows:
p
p
p
p
i =1
i =1
i =1
i =1
Δyt = β0 + ¦ χi Δyt −i + ¦δ i ΔX1t −1 + ¦ϕi ΔX 2t −1 ¦φi ΔX 3t −i + λ1 X1t + λ2 X 2t + λ3 X 3t + λ4t + ut ...4.2
Our empirical analysis was based on establishing the long-term relationship between the South African stock
market with the developed markets as well as the few selected African markets.
However, before conducting cointegration tests, it is imperative to test for unit root in order to determine the order
of integration of the variables. To determine the time series properties of the variables the Augmented Dickey Fuller
(ADF) and the Phillips Peron (PP) tests will were employed. The two tests were employed to obtain robust results.
Having established the order of integration of the variables, cointegration tests were conducted by applying the
autoregressive distributed lag (ARDL) approach developed by Pesaran et al (1996, 2001). This approach has a number
of advantages over the conventional cointegration tests like the Johansen test or the Engle Granger approach. This
approach is applicable irrespective of the order of integration of the time series, as long as none is of a second order or
more. Also, the ARDL approach is more robust and performs well even for small sample sizes. The ARDL approach also
allows the estimation of the long-run and the short-run components of the model simultaneously.
The existence of an error-correction term among a number of cointegrated variables implies that changes in the
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dependent variable are a function of both the level of disequilibrium in the cointegration relationship and the changes in
other explanatory variables. As in Masih and Masih (2002), any deviation from the long-run equilibrium will feed back to
the changes in the dependent variable in order to force the movement towards the long-run equilibrium.
In the model, the null hypothesis of no cointegration for the dependent variables is:
(H : λ1 = λ2 = λ3 = λ4 = 0) , it was tested against the alternative hypothesis (H : λ1 = λ2 = λ3 = λ4 ). The hypothesis
0
1
was examined using the standard F-statistic test as suggested by Pesaran and Pesaran (1997).
Pesaran and Pesaran (1997) and Pesaran et al (2001) provided two sets of asymptotic critical values. The first set
assumes that all variables are I(0) while the second category assumes that all variables are I(1). In the event that the
computed F-statistic is greater than the upper bound critical values, the null hypothesis of no cointegration is rejected
suggesting that there exists a long-term relationship between the variables of interest. However, if the computed Fstatistic is less than the lower bound critical value, then the null hypothesis of no cointegration will not to be rejected. In
the event that the F-statistic falls within the lower and upper bound critical values, the result is inconclusive.
4.1 Empirical Results
The empirical results begin with an analysis of the group descriptive statistics.
The descriptive statistics in table 4.1 indicates that on the selected African stock markets Nigeria has the highest
mean value whilst Namibia has the lowest. As for the standard deviation the results indicates that the Nigerian stock
market is the most volatile whilst Namibia is least volatile. In addition, the results indicate that all African stock markets
are skewed to the right indicating that there are greater chances of positive returns.
The time series properties of the data indicate that all other variables except Japan are non-stationary at level
series as reported by both the ADF and the PP. However, at first differences all variables are stationary. This therefore
justifies the use of the ARDL approach to cointegration unlike the other convention approaches which require that the
variables be integrated of order one I(1).
The results of the ARDL estimation are shown in table 4.2:
The results as reported in table 4.2 show that the calculated F-statistic (2.458338) is lower than the upper bound
but greater than the lower bound indicating that the result is inconclusive. In this case, Kremers et al (1992), state that
the error correction term needs to be analysed to establish a cointegrating equation.
The long-run coefficients and error correction terms of the ARDL model revel that Botswana, , German, Japan and
USA are highly significant impact positively on the South African Stock market. This is consistent with Aponsah (2009)
who discovered that there is evidence of linkage between the Botswana Stock Exchange and the Johannesburg Stock
Exchange. However, Namibia, Egypt and Nigeria are insignificant and impact negatively on the South African stock
market. The Mauritius stock market also impacts negatively on the South African stock market, however it is highly
significant. The results also indicate that the South African stock market is integrated in the global market as indicated by
high levels of significances for Germany, Japan and USA.
The error correction term (ECM) is statistically significant at 99 percent confidence level with a negative sign. This
therefore confirms the existence of a stable long-run relationship and points to a long-run cointegration between
variables. The ECM represents the speed of adjustment to restore equilibrium in the dynamic model following a
disturbance. The coefficient of the ECM for South Africa is -0.5643, implying that about 56 percent of deviation from the
long-run equilibrium following a short-run shock is corrected in one month.
4.2 Granger Causality Test
The empirical results show evidence of lead-lag interaction between our series except for Nigeria and Mauritius where
there is evidence of bi-directional causality. For the other African markets the results indicate that the South African stock
market leads, thus any innovations which occur in the South African stock market will be transferred to other stock
markets on the continent. However, as for developed stock markets there is bi-directional causality between South Africa
and German, whilst for Japan and the USA there is uni-directional causality running from the USA and Japan to South
Africa. This suggests that the South African stock market is well integrated into the global market.
5. Conclusion and Recommendations
The paper investigated the long-run relationship of the South African stock market with a few selected African stock
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market as well as its linkages with the developed markets represented by USA, Japan and German. The empirical results
show that the South African stock market is not fully integrated with other African stock markets. It only moves in the
same direction with the Botswana stock market attesting that potential gains from portfolio diversification between the two
markets are limited since systematic risk cannot be diversified away and arbitrage opportunities among them are not
possible. However, the South African stock market moves in different directions with the Namibian, Egypt, Nigeria and
Mauritius stock markets. This indicates that within the African region portfolio diversification is possible to some extent.
Also, the error correction term is significantly high (56 percent) suggesting that the speed of adjustment back to
equilibrium following a disturbance is rapid and there are less possibilities of achieving arbitrage opportunities in the
short-run.
The empirical results also indicate that the South African stock market is integrated with the developed markets
represented by the US, German and Japan. This shows that there are less opportunities for international investors to
diversify their portfolio and obtain long-run gains through investing in South Africa. This also indicates that the South
African stock market is affected by the movement in the developed markets in the long-run.
References
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http://dx.doi.org/10.2139/ssrn.1311325
Alagidede, P, 2008. "Month-of-the-year and pre-holiday seasonality in African stock markets,"Stirling Economics Discussion Papers
2008-23, University of Stirling, Division of Economics.
Allen, A; Otchere, I & Sebbet, L, 2010. African Financial Systems: A Review, Wharton Financial Institutions.
Altin, H., and Sahin, M., 2010. Global Market Integration around the World: The Case of Europe, America, Asia/Pacific and BRIC – T;
Middle Eastern Finance and Economics
Amponsah, W.A, 2009. Analytical and Empirical Evidence of Trade Policy Effects of Regional Integration
BIS, 2006. 76th Annual Report 1 April 2005–31 March 2006; Available on the BIS website (www.bis.org ).
Cournot, A, 1971. Researches into the Mathematical Principles of the Theory of Wealth. translated by Nathaniel T. Bacon. New York: A.
M. Kelley.
Harvey, C. R, 1995. Predictable Risk and Returns in Emerging Markets, Review of Financial Studies, Society for Financial Studies, vol.
8(3), pages 773-816.
Jefferis K, and Smith G, 2005). The Changing Efficiency of African Stock Markets. South Afr. J. Econ., 73(1): 54-67.
Kremers, J. J., Neil R. Ericsson and Juan J. Dolado, 1992. The power of Cointegration tests, Oxford Bulletin of Economics and
Statistics, 54, pp.13, pp.325 – 343.
Lovegrove, A, 2007. Financial sector integration in two regions of Sub-Saharan Africa: How creating scale in financial markets can
support growth and development. Working Paper on Regional financial Integration
Marashde, H and Shrestha, M, N, 2010. Stock Market Integration in the GCC Countries, International Research Journal of Finance and
Economics
Marshall ,A, 1930. Principles Of Economics: An Introductory Volume. Edition, 8. Publisher, Macmillan,
Masih, R and Masih, M.N, 2002. Long and short term dynamic causal transmission amongst international stock markets, Journal of
International money and Finance”, 20, pp. 563 – 587.
Persaran, M.H., Shin. Y and Smith, R, 1996. Testing for the existence of a longrunrelationship, DAE Working Paper, No.9622,
Department of Applied Economics, University of Cambridge. UK
Pesaran, H. M and Pesaran, B, 1997. Microfit 4.0, Oxford University Press, Oxford
Pretorius, E. (2002). Economic determinants of emerging stock market interdependence, Emerging Markets Review 3, 84-105.
Ripley, D. M, 1973. Systematic Elements in the Linkage of National Stock Market Indices, Review of Economics and Statistics, August,
356-361.
Sebbet, L and Otchere, I, 2006. Financial Sector Reforms in Africa: Perspectives on Issues and Policies in Annual World Bank
Conference on Development Economies: Growth and Integration, pp.81 – 119.
Senbet L.and Otchere, I, 2008. African Stock Markets, Preliminary Seminar paper for the Section on Beyond Stock Markets, IMF
Seminar, Tunisia, March.
Tai, C, 2007. Market integration and contagion: Evidence from Asian emerging stock and foreign exchange markets, Emerging Markets
review, Vol.8, No.4, pp.264 – 283.
USAID, 1998. USAID Policy Paper Financial Markets Development Bureau for Program and Policy Coordination U.S. Agency for
International Development Washington DC.
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Table 2.1: Market capitalization of listed companies (% of GDP)
Region
Country
Eastern Africa
Kenya
Tanzania
Uganda
Average
Egypt
Morocco
Tunisia
Average
Botswana
Malawi
Mauritius
Namibia
South Africa
Swaziland
Zambia
Zimbabwe
Average
Cote d’Ivoire
Ghana
Nigeria
Average
Northern Africa
Southern Africa
Western Africa
Market Capitalization as a percentage of GDP
2003 2004 2005 2006 2007 2008
28
24
34
51
50
32
7
6
4
4
6
1
1
1.12
1.17
12
10
13
19
50
19
33
49
89
87
107
53
26
44
46
75
101
76
10
9
10
14
15
16
23
34
48
59
74
49
26
26
23
36
48
27
4
6
8
19
42
37
39
41
56
83
40
6
7
6
6
8
7
161
211
233
277
294
178
10
10
8
7
7
17
8
14
11
21
67
41
70
41
44
50
59
77
59
12
13
14
24
42
30
19
30
16
25
16
21
14
16
17
22
52
23
15
20
16
24
37
25
Source: World Development Indicators
Table 2.2: Number of Listed Domestic Companies
Region
Country
Eastern Africa
Kenya
Tanzania
Uganda
Subtotal
Egypt
Morocco
Tunisia
Subtotal
Botswana
Malawi
Mauritius
Namibia
South Africa
Swaziland
Zambia
Zimbabwe
Subtotal
Cote d’Ivoire
Ghana
Nigeria
Subtotal
Northern Africa
Southern Africa
Western Africa
2003
51
6
3
20
967
53
46
355
19
40
8
13
426
5
12
81
76
38
25
200
88
Source: World Development Indicators
625
Number of Listed Companies
2004 2005 2006 2007
47
47
51
51
6
6
6
7
5
5
5
19
19
21
29
792
744
603
435
52
56
65
74
44
46
48
50
296
284
239
186
18
18
18
18
41
42
41
90
8
9
10
9
13
13
9
9
403
388
401
422
6
6
6
6
13
15
14
15
79
79
80
82
73
71
72
81
39
39
40
38
29
30
32
32
207
214
202
212
92
94
91
95
2008
53
7
6
22
373
77
49
166
19
41
14
7
425
7
81
85
38
35
213
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Table 2.3: Stocks Traded, Total value (% of GDP)
Stocks traded, total value (% of GDP)
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Average Average excluding 2008
Kenya
-22.20 -16.92 -29.18 0.58 100.87 7.60 34.88 42.10 -3.56 -35.33 7.88
12.68
Tanzania
21.26 21.26
Uganda
- -25.07 -25.07
Average -22.20 -16.92 -29.18 0.58 100.87 7.60 34.88 42.10 -3.56 -13.04 10.11
12.68
Egypt
43.80 -37.65 -37.95 3.50 152.13 110.98 127.36 11.47 51.85 -55.10 37.04
47.28
Morocco
-5.20 -19.51 -13.07 -14.87 27.64 13.95 18.76 78.84 52.46 -15.52 11.35
14.33
Tunisia
74.40 75.87 -30.09 -21.40 20.04 3.72 17.20 39.96 21.12 1.78 20.26
22.31
Average 37.67 6.24 -27.03 -10.92 66.60 42.88 54.44 43.42 38.48 -22.94 22.88
27.97
Botswana 47.80 3.87 68.93 1.53 0.23 15.61 23.21 74.07 44.43 -21.38 25.83
31.08
Malawi
- 53.56 -11.51 -31.68 45.91 27.60 55.42 154.80 109.89 25.60 47.73
50.50
Mauritius -6.40 -10.47 -12.61 17.13 37.64 29.33 13.11 49.81 51.94 -35.38 13.41
18.83
Namibia
48.10 -41.40 -35.87 -20.34 23.83 14.26 7.82 27.06 44.68 19.88 8.80
7.57
S.Africa
57.30 -2.54 25.41 -11.15 11.96 21.85 42.98 37.68 16.23 -25.72 17.40
22.19
Swaziland
11.83 14.98 3.92 10.24
13.40
Zambia
20.03 56.16 -0.55 10.33 29.04 77.27 61.96 48.17 92.29 -29.08 36.56
43.86
Average 33.37 9.87 5.63 -5.70 24.77 30.99 34.08 57.63 53.49 -8.88 23.52
27.13
Ivory Coast -6.80 -18.07 3.61 -4.26 0.81 17.11 28.31 0.27 77.02 -10.68 8.73
10.89
Ghana
-15.20 16.54 11.42 45.95 154.67 91.32 -29.85 4.97 31.84 58.06 36.97
34.63
Nigeria
-7.20 54.01 35.16 10.71 65.84 18.46 1.01 37.80 74.73 -45.77 24.48
32.28
Average -9.73 17.50 16.73 17.47 73.77 42.30 -0.18 14.34 61.19 0.54 23.39
25.93
Region Country
EA
NA
SA
W.A
Source: Allen, Otchere and Senbet (2010)
Table 2.4: African Countries Annual Stock return (%)
African Countries Annual Stock return (%)
Region
EA
NA
SA
W.A
Country
Kenya
Tanzania
Uganda
Average
Egypt
Morocco
Tunisia
Average
Botswana
Malawi
Mauritius
Namibia
S.Africa
Swaziland
Zimbabwe
Zambia
Average
Ivory Coast
Ghana
Nigeria
Average
1997
0.81
0.18
7.47
3.14
1.38
3.99
1.14
3.08
0.66
30.05
0.01
6.40
0.20
5.93
0.20
0.71
0.36
0.43
1998
0.56
0.28
5.93
3.47
0.95
3.45
1.35
2.44
0.38
43.45
0.01
3.07
0.08
7.25
0.31
0.80
0.50
0.54
1999
0.57
0.08
0.33
9.96
6.37
2.02
6.12
0.67
1.80
0.66
54.75
0.01
3.80
0.38
8.87
0.68
0.32
0.42
0.47
2000
0.37
0.44
0.41
11.14
2.95
3.22
5.77
0.77
1.69
0.56
58.32
0.01
3.80
0.25
9.34
0.32
0.20
0.57
0.36
2001
0.31
0.08
0.13
3.99
2.58
1.58
2.72
1.08
2.46
0.22
58.81
0.02
3.77
1.46
11.39
0.08
0.25
1.03
0.45
Source: Allen, Otchere and Senbet (2010)
626
2002
0.28
0.19
0.01
0.16
2.91
1.45
1.05
1.80
0.93
1.25
0.34
0.04
71.10
0.77
14.91
0.05
12.11
0.14
0.18
0.80
0.38
2003
1.40
0.19
0.53
3.95
1.39
0.66
2.00
1.05
1.89
0.24
0.03
61.69
0.02
11.35
0.25
10.43
0.18
0.60
1.27
0.68
2004
2.14
0.15
0.76
7.11
2.95
0.80
3.62
0.51
1.57
0.34
0.27
75.38
0.00
18.18
0.12
10.12
0.30
0.74
1.90
0.98
2005
2.69
0.10
0.03
0.94
28.31
6.97
1.57
12.28
0.43
2.40
0.28
0.09
82.67
0.00
2.88
0.20
11.97
0.19
0.63
1.73
0.85
2006
5.78
0.08
0.06
1.97
44.16
20.57
1.69
22.14
0.66
2.14
0.45
0.23
121.23
0.00
9.70
0.21
17.85
0.62
0.41
2.42
1.15
2007
4.89
4.89
40.68
34.98
1.86
25.84
0.89
5.44
0.26
150.05
0.00
0.63
31.45
0.80
0.73
10.11
3.88
2008
4.17
4.17
42.77
25.40
3.72
23.96
1.11
4.66
1.40
0.22
145.07
0.00
30.49
1.35
0.93
9.41
3.89
Mediterranean Journal of Social Sciences
E-ISSN 2039-2117
ISSN 2039-9340
Vol 5 No 2
January 2014
MCSER Publishing, Rome-Italy
Table: 4.1 Group Descriptive Statistics
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
Probability
Sum
Sum Sq. Dev
Observations
South Africa
11391.66
10108.61
21953.80
7364.171
3919.225
1.370473
3.797880
26.82509
0.000001
899940.9
1.20E+09
79
Botswana
2534.964
2485.930
4501.530
1389.020
748.0475
0.429631
2.920956
2.450903
0.293625
200262.2
43646849
79
Namibia
109.2077
102.6200
161.6000
59.51000
39.03024
0.117623
1.299925
9.695923
0.007844
8627.410
118822.0
79
Egypt
1056.227
987.6000
1846.890
643.0700
288.0261
0.995250
3.511535
13.90320
0.000957
83441.91
6470805.
79
Mauritius
536.7011
454.4600
871.3400
340.9200
172.5271
0.566072
1.772875
9.175799
0.010174
42399.39
2321717
79
Japan
12338.95
11492.54
20337.32
7831.420
2868.592
0.737323
2.872675
7.211357
0.027169
974777.0
6.42E08
79
German
4855.049
4818.300
7644.550
2423.870
1336.865
0.280206
2.207013
3.103684
0.211857
393548.9
1.39E+08
79
Nigigeria
16444.96
13962.04
33096.37
5892.790
7185.834
0.214466
1.714748
6.043023
0.048728
1299152.
4.03E+09
79
Usa
1162.380
1156.850
1517.680
815.2800
165.3675
-0.039301
2.596379
0.556581
0.757077
91828.01
2133019
79
Table 4.1: Unit Root Test Results
ADF Test Results
Philips Peron
t-statistic
Inference
t-statistic
Inference
SA
-0.741762
Nonstationary
-0.853248
Nonstationary
Botswana
-1.219130
Nonstationary
-1.166598
Nonstationary
Namibia
1.912389
Nonstationary
1.771341
Nonstationary
Nigeria
-1.770815
Nonstationary
-2.120665
Nonstationary
Egypt
-2.196183
Nonstationary
-2.037818
Nonstationary
Mauritania
-0.952408
Nonstationary
-1.486608
Nonstationary
US
-1.950373
Nonstationary
-2.094554
Nonstationary
German
-1.730227
Nonstationary
-1.906906
Nonstationary
Japan
-2.750545*
Stationary
-2.584050*
Stationary
Notes: *** Significant at 1% level; ** Significant at 5% level and Significant at 10% level
Variables
Table 4.2: F-statistic for testing the Existence of a Long-run Relationship
Wald Test:
Equation: Untitled
Test Statistic
Value
Df
F-statistic
2.458338
(9, 60)
Chi-square
13.12504
9
Null Hypothesis: C(1)=0, C(3)=0, C(5)=0, C(7)=0, C(9)=0,
C(11)=0, C(13)=0, C(15)=0, C(17)=0
Probability
0.0445**
0.0570
Notes:
•
The relevant critical value bounds are obtained from Pesaran and Pesaran (1997)
•
The critical values in the case of 9 regressors with intercept and trend are 2.192 – 3.285 at a 10% level and 2.467
– 3.614 at a 5% significance level.
•
denotes that the F-statistic falls above the 90% upper bound, while ** denotes that the F-statistic falls above the
95% upper bound.
•
The ARDL model is based on the Akaike Information Criteria (AIC) as it employs the maximum relevant lags.
Table 4.3: Estimated Long-run Coefficients and Error Correction terms for the ARDL Model
Dependent Variable: South Africa
Regressors:
1.026375
Botswana
[2.792906]**
-8.440770
Namibia
[-0.665720]
627
E-ISSN 2039-2117
ISSN 2039-9340
Mediterranean Journal of Social Sciences
MCSER Publishing, Rome-Italy
Vol 5 No 2
January 2014
-0.045304
[-1.453435]
-8.593770
Mauritius
[-2.356453]**
1.539464
German
[4.267102]***
0.018200
Japan
[2.025012]**
2.311554
USA
[3.215012]***
-2.4567
Constant
[-0.3697]
0.10392
Trend
[4.4534]***
-0.564340
ECM(-1)
[-5.277988]***
R-Squared
0.665958
Adjusted R-Squared
0.578340
Note: t-statistics are represented in squared brackets. ***, **, * represents 1%, 5% and 10% significant levels respectively
Egypt
628